关键词: 微博, 观点识别, 图排序, 受限玻尔兹曼机

Abstract: With the explosive growth of online social networks, Sina Weibo has become an important platform for people to express their opinions and emotion. Weibo not only reflects the opinions of the users, but also conveys   the opinions to other users, which can affect the opinions of other users. However, the typical features of Weibo such as short and flexiblity, bring some new challenges for opinion mining. When traditional approaches focusing on building a sophisticated feature space for text are used to analyze Weibo, the performance is usually poor. To solve this problem, this paper proposes an opinion mining method based on graph ranking for Sina Weibo. Firstly, utilize Boolean model to represent Weibo, and assign a pseudo label for each Weibo by logistic regression. Then, construct Weibo relationship graph based on context, and take advantage of restricted Boltzmann machine to extract high dimensional features. Finally, mine opinion for Weibo based on graph ranking. The experimental results show that the proposed method can mine opinion for Weibo effectively.

Key words: Weibo, opinion mining, graph ranking, restricted Boltzmann machine